{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ff89916-24da-453f-9649-88474320ef93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance of the ecg signal = 1575.5434384164223\n",
      "Sum of variance across all SWT coefficients = 1575.5434384188645\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rI2JtRKzdvHnzbIYvSdK80fMkn5lbMvMgYAlwSET8AfBm4LHA/wD2AN40zWWempnLMnPZ4sWLZzpkSZLmpb71rs/Mm4ELgWdl5nWlSf4e4OPAIaXaRmC/2mxLSpkkSZpCr3vXL46I3cr4zsDTgR+NnWePiACOBi4vs5wHHFd62R8K3JKZ1/UyZkmS5qte967fB1gTEYuofmCclZnnR8RXI2IxEMClwKtK/QuA5wDrgDuBV/Q4XkmS5q2eJvnMvAw4uEP5Uyeon8AJsx2XJElN5BXvJElqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXttLIyAirVq1iZGSk36FIUkfb9zsAaT4aGRlh+fLljI6OMjAwwNDQEIODg/0OS5LuxyN5aSsMDw8zOjrKli1bGB0dZXh4uN8hSdID9DTJR8ROEfHdiPhBRFwREW8r5Y+MiO9ExLqI+HREDJTyHcvzdWX60l7GK02k1WoxMDDAokWLGBgYoNVq9TskSXqAXh/J3wM8NTOfABwEPCsiDgXeDXwgM38XuAl4Zan/SuCmUv6BUk/qu8HBQYaGhnjHO95hU72kOaun5+QzM4Hby9MdypDAU4GXlPI1wEnAKcBRZRzgbODDERFlOVJfDQ4OmtwlzWk9PycfEYsi4lJgE/Bl4GfAzZl5X6myAdi3jO8L/AKgTL8F2LOnAUuSNE/1PMln5pbMPAhYAhwCPHZblxkRKyNibUSs3bx587YuTpKkRuhb7/rMvBm4EBgEdouIsVMHS4CNZXwjsB9Amf5Q4FcdlnVqZi7LzGWLFy+e7dAlSZoXet27fnFE7FbGdwaeDlxFlexfWKodD3yujJ9XnlOmf9Xz8ZIkdafXF8PZB1gTEYuofmCclZnnR8SVwKci4h+B7wOnlfqnAf8ZEeuAXwPH9jheSZLmrV73rr8MOLhD+dVU5+fby+8G/rwHoUmS1Dhe8U6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL2lOGxkZYdWqVYyMjPQ7FGne6fVd6CSpayMjIyxfvpzR0VEGBgYYGhpicHCw32FJ84ZH8pLmrOHhYUZHR9myZQujo6MMDw/3OyRpXjHJS5qzWq0WAwMDLFq0iIGBAVqtVr9DkuYVm+slzVmDg4MMDQ0xPDxMq9WyqV6aJpO8pDltcHDQ5C5tJZvrJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIbqaZKPiP0i4sKIuDIiroiI15bykyJiY0RcWobn1OZ5c0Ssi4gfR8QzexmvJEnzWa9vNXsf8PrMvCQiHgxcHBFfLtM+kJnvrVeOiAOBY4HfB34H+EpE/F5mbulp1JIkzUM9PZLPzOsy85IyfhtwFbDvJLMcBXwqM+/JzJ8D64BDZj9SSZLmv76dk4+IpcDBwHdK0asj4rKI+FhE7F7K9gV+UZttAx1+FETEyohYGxFrN2/ePJthS5I0b/QlyUfErsBngNdl5q3AKcCjgYOA64D3TWd5mXlqZi7LzGWLFy+e6XAlSZqXep7kI2IHqgR/ZmaeA5CZN2Tmlsz8DfAfjDfJbwT2q82+pJRJkqQp9Lp3fQCnAVdl5vtr5fvUqr0AuLyMnwccGxE7RsQjgQOA7/YqXkmS5rNe964/DHg58MOIuLSUvQV4cUQcBCSwHvgrgMy8IiLOAq6k6pl/gj3rJUnqTk+TfGZeBESHSRdMMs87gXfOWlCSJDWUV7yTJKmhpnUkX5rUnwHsmZlvioj9y6RfZuZ9Mx2cJEnael0fyUfEh4CLgVXA35biM4GfAy+Z+dAkSdK26CrJR8QrgFdTnU+vn1P/SHn+/JkPTZIkbYtuj+T/mqrn+1lt5cPl8QkzFZAkSZoZ3Sb5A8vjCW3lN5THfZAkSXNKt0l+rIn+rrbypeUxZyQaSZI0Y7pN8j8rj8ePFZSr1P1LefrTmQxKkiRtu26T/FlUR/MfZvyofQPV3+kS+H8zH5okSdoW3Sb5f6a6Zny9d/3Y+Frg/RPMJ0mS+qSri+Fk5j0RcQTwWuBI4GHAJuB84OTMvGf2QpQkSVuj6yveZeZdwLvKIEmS5riuknxELKP6G93V5SYzY+WHA48CrszMtbMToiRJ2hrdnpN/D/Bxqmb6uj2B1cC7ZzAmSZI0A7pN8n9YHi9sK/9aeXz8zIQjSZJmSrdJ/iHlcVFb+fZt0yVJ0hzRbZLfXB5XtpX/ZXncNDPhSJKkmdJt7/pvAC8C3hERy4ErgccBR1BdDOfrsxOeJEnaWt0m+fcAf0bVXN8qA1QXw7mX6mI5kiRpDumquT4zv091JP9rxq90F8CvgBdl5qWzFaAkSdo607kYzrkR8QXgMGBvqtvMfjMz756t4CRJ0tbrOskDlIQ+NEuxSJKkGdR1ko+IPwZeDjwC2Kltcmbm8pkMTJIkbZtuL2t7HNUV7zpOZvz2s5IkaY7o9kj+RMZvMStJkuaBbpP8Uqqj9fcAZwB34NG7JElzWrdJ/mqqi9+8MzNvn8V4JEnSDOn2srbvpmquf8ksxiJJkmZQt0fyTwVuBk6JiP8J/JjqSndjMjNfOcOxSZKkbdBtkj+e8XPwf1SGdlMm+YjYDzid6mI6CZyamSdHxB7Ap6nO/a8HjsnMmyIigJOB5wB3Aisy85IuY5YkaUHrtrke7n852/ahW/cBr8/MA4FDgRMi4kCq3vtDmXkA1cV2Tiz1nw0cUIaVwCnTWJckSQtat0fyj5yJlWXmdcB1Zfy2iLgK2Bc4ivGb3qwBhoE3lfLTMzOBb0fEbhGxT1mOJEmaRFdJPjOvmekVR8RS4GDgO8DetcR9PVVzPlQ/AH5Rm21DKTPJS5I0hWlduz4idqNqOt+5fVpmdn1P+YjYFfgM8LrMvLU69f7b5WRETOs/+BGxkqo5n/333386s0qS1FjdXtZ2B+AjwHF0Po+f01zWZ4AzM/OcUnzDWDN8ROwDbCrlG4H9arMvKWX3X3nmqcCpAMuWLfMiPZIk0X3Hu78FXgEsYhs635Xe8qcBV2Xm+2uTzqPqwU95/Fyt/LioHArc4vl4SZK6021z/bFUR+s/AA4q4+dS/bVtA3BRl8s5jOpOdj+MiEtL2VuAdwFnRcQrgWuAY8q0C8o61lH9he4VXa5HkqQFr9sk/+jy+EKqhEtmvjAingt8lqon/JQy8yImPup/wK1qS6/6E7qMUZIk1XTbXL9DebwG2AIQETsDX6Fqwn/bzIcmSZK2RbdH8jcBi6l61f8a2Av4e2DsZjW/O/OhSZKkbTGdu9AtpvqP+iXAMxlvok/g5zMfmiRJ2hbdNtd/GfgJ8FjgvcBvuH+v+rfPfGiSJGlbdHvFu7cCbx17HhFPoeqEdx/w2cz85uyEJ0mStta0rng3piR1E7skSXPYhEk+Io4DyMzTx8Ynk5mnz2RgkiRp20x2JL+a6tz76WV8ssvFZqknSZLmiKma62OCcUmSNMdNluRfMcG4JEmaByZM8pm5BiAitgMuLMWbMvPuXgQmSZK2TTf/kw+qi91cDewzu+FIkqSZMmWSz8wtwA1Uyf76WY9IkiTNiG6vePfJ8njkbAUiSZJmVrcXw/kJ1Y1p1kTEcuD7wF31Cv5PXpKkuaXbJH8K1X/hA/jLDtP9n7wkSXPMdC5rG22PkiRpDus2yfs/eUmS5plu70K3ZrYDkSRJM6vb3vWSJGme6TrJR8TLIuKSiLgjIra0DffNZpCSJGn6umquj4hjqHrPj/WwlyRJc1y3R/InlMex/8Yn8KsyfjNwzQzGJEmSZkC3Sf7xVIn9aWMFmbkYeCtwL/C8mQ9NkiRti26T/C7l8RKqZE9ELALeBywGPjTzoUmSpG3R7f/kbwV2pzoffxvwYODZwC1l+pNmPjRJkrQtuj2S/2V5fBhwVRn/HDBcxn89gzFJkqQZMGGSj4iltaffpzqKfxJVL/uoDQBeLEeSpDlmsub6n0XEN4Ezgb8D3gjclpl3RsRDgRcB9wHnAu+e9UglSdK0TJbkAzisDKPABcAZEXF+Zr4LeFcP4pMkSVtpsnPyGxhvkt8ROBo4G7g+Ij4SEYdPd2UR8bGI2BQRl9fKToqIjRFxaRmeU5v25ohYFxE/johnTnd9kiQtZBMm+czcH3gy1b3kb2Q84e9OdU/5r0XE1RHxjoh4bJfrWw08q0P5BzLzoDJcABARBwLHAr9f5vm38rc9SZLUhUl712fmRZl5ArAP1V/mTqf6O91Ywl8KvAW4fKJltC3v63TfE/8o4FOZeU9m/hxYBxzS5byaZ0ZGRli1ahUjIyP9DkWSGqPbW81uAb4EfCkidgSeC7yG6kh/Jq5l/+qIOA5YC7w+M28C9gW+XauzoZQ9QESsBFYC7L///jMQjnppZGSE5cuXMzo6ysDAAENDQwwODvY7LEma96Z1q9mI2JWqV/1K4I8pV7/bRqcAjwYOAq6juoretGTmqZm5LDOXLV68eAZCUi8NDw8zOjrKli1bGB0dZXh4uN8hSVIjTHkkHxEDwJHAi4HnADuNTapV2+o21sy8obau/wDOL083AvvVqi4pZWqYVqvFwMDAb4/kW61Wv0OSpEaYMMlHxDOoEvsLqC5jC/dP7OuAM4AzMvPqrQ0gIvbJzOvK0xcwfn7/POATEfF+4HeAA4Dvbu16NHcNDg4yNDTE8PAwrVbLpnpJmiGTHcl/kQfeP/5G4NNUif07011ZRHwSaAF7RcQGqrvYtSLioLKu9cBfAWTmFRFxFnAl1UV3Tih9A9RAg4ODJndJmmGR2fm0ekT8pozeTXVUfQbwxcy8r0exbZVly5bl2rVr+x2GJEk9ExEXZ+ay9vLJjuQvpErsZ2fmbbMWmSRJmhUTJvnMXN7LQCSpKUZGRuxjojmh2/vJS5K64HUfNJdM63/ykqTJed0HzSUmeUmaQWPXfVi0aJHXfVDf2VwvSTPI6z5oLjHJS9IM87oPmitsrpckqaFM8pIkNZRJXpKkhjLJS5LUUCZ5SZIayiQvSVJDmeQlSWook7wkSQ1lkpckzUsjIyOsWrWKkZGRfocyZ3nFO0nSvOPd/rrjkbwkad7xbn/dMclLkuYd7/bXHZvrJUnzjnf7645JXpI0L3m3v6nZXC9JUkOZ5CVJaiiTvCRJDWWSlySpoUzykiQ1lElekqSGMslLktRQJnlJkhqqp0k+Ij4WEZsi4vJa2R4R8eWI+Gl53L2UR0R8KCLWRcRlEfHEXsYqSdJ81+sj+dXAs9rKTgSGMvMAYKg8B3g2cEAZVgKn9ChGSZIaoadJPjO/Dvy6rfgoYE0ZXwMcXSs/PSvfBnaLiH16EqgkSQ0wF87J752Z15Xx64G9y/i+wC9q9TaUMkmS1IW5kOR/KzMTyOnOFxErI2JtRKzdvHnzLEQmSdL8MxeS/A1jzfDlcVMp3wjsV6u3pJQ9QGaempnLMnPZ4sWLZzVYSZLmi7mQ5M8Dji/jxwOfq5UfV3rZHwrcUmvWlyRJU+jp/eQj4pNAC9grIjYAbwXeBZwVEa8ErgGOKdUvAJ4DrAPuBF7Ry1glSZppIyMjDA8P02q1GBwcnPX19TTJZ+aLJ5i0vEPdBE6Y3YgkSeqNkZERli9fzujoKAMDAwwNDc16op8LzfWSJDXe8PAwo6OjbNmyhdHRUYaHh2d9nSZ5SZJ6oNVqMTAwwKJFixgYGKDVas36OnvaXC9J0kI1ODjI0NBQc8/JS5K0kA0ODvYkuY+xuV6SpIYyyUuSGBkZYdWqVYyMjPQ7FM0gm+slaYHrx1+71BseyUvSAtePv3apN0zykrTA9eOvXeoNm+slaYHrx1+71BsmeUlSz//apd6wuV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGmr7fgcwJiLWA7cBW4D7MnNZROwBfBpYCqwHjsnMm/oVoyRJ88lcO5I/IjMPysxl5fmJwFBmHgAMleeSJKkLcy3JtzsKWFPG1wBH9y8USZLml7mU5BP474i4OCJWlrK9M/O6Mn49sHenGSNiZUSsjYi1mzdv7kWskiTNeXPmnDxweGZujIiHAV+OiB/VJ2ZmRkR2mjEzTwVOBVi2bFnHOpIkLTRz5kg+MzeWx03AucAhwA0RsQ9AedzUvwglSZpf5kSSj4hdIuLBY+PAM4DLgfOA40u144HP9SdCSZLmn7nSXL83cG5EQBXTJzLzixHxPeCsiHglcA1wTB9jlCRpXpkTST4zrwae0KH8V8Dy3kckSdL8Nyea6yVJ0swzyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokL0lSQ5nkJUlqKJO8JEkNZZKXJKmhTPKSJDWUSV6SpIYyyUuS1FAm+QYaGRlh1apVjIyM9DsUSVIfbd/vADSzRkZGWL58OaOjowwMDDA0NMTg4GC/w5Ik9YFH8g0zPDzM6OgoW7ZsYXR0lOHh4X6HJEnqE5N8w7RaLQYGBli0aBEDAwO0Wq1+hyRJ6hOb6xtmcHCQoaEhhoeHabVaNtVL0gJmkm+gwcFBk7skyeZ6SZKayiQvSVJDmeQlSWook7wkSQ1lkpckqaHmRZKPiGdFxI8jYl1EnNjveCRJmg/mfJKPiEXAvwLPBg4EXhwRB/Zi3V4DXpI0n82H/8kfAqzLzKsBIuJTwFHAlbO5Uq8BL0ma7+b8kTywL/CL2vMNpWxWeQ14SdJ8Nx+S/JQiYmVErI2ItZs3b56RZXoNeEnSfDcfmus3AvvVni8pZb+VmacCpwIsW7YsZ2KlXgNekjTfzYck/z3ggIh4JFVyPxZ4SS9W7DXgJUnz2ZxP8pl5X0S8GvgSsAj4WGZe0eewJEma8+Z8kgfIzAuAC/odhyRJ80kjOt5JkqQHMslLktRQJnlJkhrKJC9JUkOZ5CVJaiiTvCRJDWWSlySpoUzykiQ1lElekqSGiswZuZ/LnBERm4Fr+h3HHLAXcGO/g1gA3M+94X7uDfdzb8zGfn5EZi5uL2xcklclItZm5rJ+x9F07ufecD/3hvu5N3q5n22ulySpoUzykiQ1lEm+uU7tdwALhPu5N9zPveF+7o2e7WfPyUuS1FAeyUuS1FAm+QaJiP0i4sKIuDIiroiI1/Y7piaLiEUR8f2IOL/fsTRZROwWEWdHxI8i4qqIGOx3TE0UEf+nfG9cHhGfjIid+h1TE0TExyJiU0RcXivbIyK+HBE/LY+7z9b6TfLNch/w+sw8EDgUOCEiDuxzTE32WuCqfgexAJwMfDEzHws8Aff5jIuIfYHXAMsy8w+ARcCx/Y2qMVYDz2orOxEYyswDgKHyfFaY5BskM6/LzEvK+G1UX4b79jeqZoqIJcBzgY/2O5Ymi4iHAk8GTgPIzNHMvLmvQTXX9sDOEbE98CDgl32OpxEy8+vAr9uKjwLWlPE1wNGztX6TfENFxFLgYOA7fQ6lqT4IvBH4TZ/jaLpHApuBj5dTIx+NiF36HVTTZOZG4L3AtcB1wC2Z+d/9jarR9s7M68r49cDes7Uik3wDRcSuwGeA12Xmrf2Op2ki4khgU2Ze3O9YFoDtgScCp2TmwcAdzGLT5kJVzgkfRfWj6neAXSLiZf2NamHI6i9us/Y3N5N8w0TEDlQJ/szMPKff8TTUYcDzI2I98CngqRFxRn9DaqwNwIbMHGuROpsq6WtmPQ34eWZuzsx7gXOAP+5zTE12Q0TsA1AeN83WikzyDRIRQXXu8qrMfH+/42mqzHxzZi7JzKVUnZO+mpke9cyCzLwe+EVEPKYULQeu7GNITXUtcGhEPKh8jyzHDo6z6Tzg+DJ+PPC52VqRSb5ZDgNeTnVkeWkZntPvoKRt9L+BMyPiMuAg4J/6G07zlJaSs4FLgB9S5QavfjcDIuKTwAjwmIjYEBGvBN4FPD0ifkrVivKuWVu/V7yTJKmZPJKXJKmhTPKSJDWUSV6SpIYyyUuS1FAmeUmSGsokr8aLiJMiImvDvRFxU7nj1uqIOKzfMTZNRLRq+/uktvKTyrC0w3xj8wz3MNz2GFbU4ljRrzjaRcTwWFz9jkXzh0leC9H2wG7AgVQXorgoIj5ULgKi2dUC3lqGpX2NRFoATPJaaN5G9b7fB3gVcEsp/9/A3/crqKbJzOHMjDKcNI35xuZpzV500sJhkteCk5XrM/PfgRW1SW+KiD3GnkTE7hHxzxHx44i4OyJujYivRcTR7cuMiIdHxAci4ie1updExPFt9VZExDcj4raIuCcifhYRH4yIvdrqra81Gf9hRHw9Iu6KiKsi4vkRMRAR74mITRFxY0T8Z0TsVpu/3lz+toh4Y0T8vKyz45UQy3K/Uk5ljEbEtRFxWnuzekQ8ISLOiYiNZXm/Ktv67+XeCR2b68u1/t9aW9SFtTqtUqdjc31EPD4iPhkR15XTLTdGxOcj4k/a6tVPzRwVEf8aETdExC0R8d8RcUD7dk9HRLykvBa3lG3/SUS8MyIeVKbvUF6TjIgftc372Fpsp9fKD42Ic0uc90bEL6M6jbQUaVtlpoNDowfgJKq7PCVwUofpP65N//NS9jBgXa28fXhDbf7fpbpdZKd6q2v1/n2S5a0HHl6ru742bXNb3VHggg7LOL02f2uS+RO4D3h6rf6bJ4nt18CBpd6DJlje2LBrh/Wf1GGb2odWqTP2fLgW2xHA3RPMtwV46QSv9U0d6v8IWDTF+2VFrf6KWvm/TBL/94CdS73318oPrs3/9g7be0x5LTot81fAY2rzD49N6/dnymH+DB7JS9UX/5il5fHtwKOpksifATsDS4Cvl+n/GOUuUsCHGL8f9LlUSf/BwJOBrwJE1blvZalzDdU12PcAPl7KHlHW2ckXgN2pkgfADsAzgeeV9V5Tyl80Qb+CXYHnAA8B3lTKFgHvLrHV130z8BTgocA/lrLdgQ+W8ccBY60ObwR2AhYDhwOrqBJWR1nd0OdttaIjcrx5fnii+ah+HO1Yxv+6bMfRZV3bAf8Sne8xfwvVfv4dxm+28hjgkEnW1VFEHAq8ujxdDTyc6gfPG0rZshIbwMdqs76kNv7i8vgz4Gvl6P8UqtfiEuCxVNt5BNUPuT2Af55urFKdSV7q/Dl4XnlcRHXr3ruobnv65FI+ADwlInYGnl7Kbgdenpk/y8zbM/MbmTnWLPvc2rJPzswfZOZNwN8wfi/piW4m9PbMvBn4Sq3sW5l5fmZuAr5Vi+nhHeY/JzO/kJm3USWNDaX84IjYk+oHw/al7PTM/Hpm3kp1VHxjKT8iInYCfsF4Ij+W6kfDU4EbMvMtmXn3BNuwVSLi94CxJvbLMvMjmXlbZn4OOL+U7w4Mdpj9vWU/X0fV8jHmEVsRyvNq4yuoWm7u5P5J+BkAmXk51ZE9lB9eEfE/qH78QdW6k1Q3lBo7PfREqh+b9wAXUr2WMP7ekraKSV6qjqDG/Lw8PqyL+fak+pIeS5DXZuYdE9RdXBu/dmykJO9bp1jn+vJ4V63smtr4aG18Rx6ovr5kPMlDdVQ+UWxbgI3l6fbAHuVHxauokv8TqY7MPw38NCK+EREPmWAbtlbH2Ir6Pui0735cG6+/LjttRRzdvh/GjB3N7wf8CeNH9L+hagnodpk7TdBKIXXFJK8FLSL+jPEjrDuBoTK+qTzeAexYa1aOzAxgu8z8V6rz1WNHtvuPdcDqYFNtfP/a+nejan5ur/NbmdmpCXzCZvEO6usLqtMOY26cJLZFwL619f26xHMaVYvBH1KdyvhQqXM4cMIUsUz3P94dY+vwvNO+u3cb1jtZHC9rfz+U90T9NMCnqPoRALyU6tw7wH9n5tiPrPoyPzrBMreb5IejNCWTvBac0ny6d0S8ivufP31XaUKH8abgXYCPRsR+pef0IyNiJfADgMy8C/hyqbsrsCYiHhURu5Re08eVaf9VW89rouoxvxvwXiA61JlJL4iIZ0TEg6nOIY8l+e9n5q+ALzH+o+HlEXF4OSL/B8bPv381M++OiL0i4r1UzeObgM8Dn6utqz0Rt/tVbfzxETHpd1Bm/hT4Sa3+yojYNSKeBxxZym+iul/3bDq/Nv6OiDgsInaMiD0i4lkR8QmqZD4W983AOeXpX1D1C4D7v9++RRU7wHGl5/6u5b3zpIj4Z8b7Qkhbp989/xwcZnvg/j2uJxpOBqI2z97A1ZPNU6s7q73ra2WtCZa7ula+tEPdjR3Wt7W965dMsR+P7rD+k2rrWTbFvpzJ3vWtCcpXTPF+WdGpLvBvU2z7irblLG+bfiMw0FbnxWUbJlpm/XUebt9fDg5TDR7JayHaQtWL/EpgDXBYZr42M3/bpJuZN1AlpPcw3iHqdqqjyk9QdTobq7uOqhf3ycBPa3UvpepENVbvr4BXUB113k7VnHx1mW9ZZl4/C9sK8B/A/6HqbzAKXAYclZljLRBk5irgKKp/A9xC9SNgA9WR5xMz88pS9SbgA1Qdy26k2pe3UR2VviQzPztZIJm5FngNVQ/zeyerW5vnQqqm8E9T/ZgaO3XwX1SJ/MxulrOtMvN/AS8Dvka1j+6l2kcXUv3T4Atts3yV8f4UAGdmZr3/BJn5SarTHJ8BbqDats3AWqp/P7xvprdDC0vUvtckNUS5uMzYD4y35TSuOiepOTySlySpoUzykiQ1lM31kiQ1lEfykiQ1lElekqSGMslLktRQJnlJkhrKJC9JUkOZ5CVJaqj/D8fqjo/flBVqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pywt\n",
    "import pywt.data\n",
    "\n",
    "ecg = pywt.data.ecg()\n",
    "\n",
    "# set trim_approx to avoid keeping approximation coefficients for all levels\n",
    "\n",
    "# set norm=True to rescale the wavelets so that the transform partitions the\n",
    "# variance of the input signal among the various coefficient arrays.\n",
    "\n",
    "coeffs = pywt.swt(ecg, wavelet='sym4', trim_approx=True, norm=True)\n",
    "ca = coeffs[0]\n",
    "details = coeffs[1:]\n",
    "\n",
    "print(\"Variance of the ecg signal = {}\".format(np.var(ecg, ddof=1)))\n",
    "\n",
    "variances = [np.var(c, ddof=1) for c in coeffs]\n",
    "detail_variances = variances[1:]\n",
    "print(\"Sum of variance across all SWT coefficients = {}\".format(\n",
    "    np.sum(variances)))\n",
    "\n",
    "# Create a plot using the same y axis limits for all coefficient arrays to\n",
    "# illustrate the preservation of amplitude scale across levels when norm=True.\n",
    "ylim = [ecg.min(), ecg.max()]\n",
    "\n",
    "fig, axes = plt.subplots(len(coeffs) + 1)\n",
    "axes[0].set_title(\"normalized SWT decomposition\")\n",
    "axes[0].plot(ecg)\n",
    "axes[0].set_ylabel('ECG Signal')\n",
    "axes[0].set_xlim(0, len(ecg) - 1)\n",
    "axes[0].set_ylim(ylim[0], ylim[1])\n",
    "\n",
    "for i, x in enumerate(coeffs):\n",
    "    ax = axes[-i - 1]\n",
    "    ax.plot(coeffs[i], 'g')\n",
    "    if i == 0:\n",
    "        ax.set_ylabel(\"A%d\" % (len(coeffs) - 1))\n",
    "    else:\n",
    "        ax.set_ylabel(\"D%d\" % (len(coeffs) - i))\n",
    "    # Scale axes\n",
    "    ax.set_xlim(0, len(ecg) - 1)\n",
    "    ax.set_ylim(ylim[0], ylim[1])\n",
    "\n",
    "\n",
    "# reorder from first to last level of coefficients\n",
    "level = np.arange(1, len(detail_variances) + 1)\n",
    "\n",
    "# create a plot of the variance as a function of level\n",
    "plt.figure(figsize=(8, 6))\n",
    "fontdict = dict(fontsize=16, fontweight='bold')\n",
    "plt.plot(level, detail_variances[::-1], 'k.')\n",
    "plt.xlabel(\"Decomposition level\", fontdict=fontdict)\n",
    "plt.ylabel(\"Variance\", fontdict=fontdict)\n",
    "plt.title(\"Variances of detail coefficients\", fontdict=fontdict)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05089c4d-ae2f-4602-95c2-6acc0bac6718",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
